python-scikit-learnPandas dataframe and Sklearn linear regression example

import pandas as pd
from sklearn import linear_model, model_selection

data = pd.read_csv('/var/www/examples/housing.csv')

X = data.loc[:, ['housing_median_age', 'total_rooms']]
y = data.median_house_value

X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)

model = linear_model.LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)ctrl + cgithub

`import pandas as pd` load Pandas module	`from sklearn import` import module from scikit-learn
`pd.read_csv` read data from csv file into dataframe	`['housing_median_age', 'total_rooms']` select those columns to be used as features
`data.median_house_value` target variable	`model_selection.train_test_split` splits given `X` and `y` datasets to test (25% of values by default) and train (75% of values by default) subsets
`linear_model.LinearRegression` initialize linear regression model	`.fit(` train model with a given features and target variable dataset
`.predict(` predict target variable based on given features dataset

Usage example

import pandas as pd
from sklearn import linear_model, model_selection

data = pd.read_csv('/var/www/examples/housing.csv')

X = data.loc[:, ['housing_median_age', 'total_rooms']]
y = data.median_house_value

X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)

model = linear_model.LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print(len(y_pred))

output

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